Predicting patient reported outcome measures: a scoping review for the artificial intelligence-guided patient preference predictor.

ABSTRACT:

Background

The algorithmic patient preference predictor (PPP) has been proposed to aid in decision making for incapacitated patients in the absence of advanced directives. Ethical and legal challenges aside, multiple practical barriers exist for building a personalized PPP. Here, we examine previous work using machine learning to predict patient reported outcome measures (PROMs) for capacitated patients undergoing diverse procedures, therapies, and life events. Demonstrating robust performance in predicting PROMs for capacitated patients could suggest opportunities for developing a model tailored to incapacitated ones.

Methods

We performed a scoping review of PubMed, Embase, and Scopus using the PRISMA-ScR guidelines to capture studies using machine learning to predict PROMs

SUBMITTER: Balch JA 

PROVIDER: S-EPMC11573790 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature